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Erik Laurini

Researcher at University of Trieste

Publications -  142
Citations -  3026

Erik Laurini is an academic researcher from University of Trieste. The author has contributed to research in topics: Dendrimer & Moiety. The author has an hindex of 27, co-authored 133 publications receiving 2376 citations. Previous affiliations of Erik Laurini include Information Technology University.

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A Dual Targeting Dendrimer-Mediated siRNA Delivery System for Effective Gene Silencing in Cancer Therapy

TL;DR: The targeted system had enhanced siRNA delivery, stronger gene silencing, and more potent anticancer activity compared to nontargeted or covalent dendrimer-based systems, and can be further developed to provide RNAi-based personalized medicine against cancer.
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Smoothened (SMO) receptor mutations dictate resistance to vismodegib in basal cell carcinoma.

TL;DR: Targeting this pathway with vismodegib, a novel SMO inhibitor, results in impressive tumor regression in patients harboring genetic defects in this pathway, however, a secondary mutation in SMO has been reported in medulloblastoma patients following relapse on vismODEgib to date.
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Efficient delivery of sticky siRNA and potent gene silencing in a prostate cancer model using a generation 5 triethanolamine-core PAMAM dendrimer.

TL;DR: The low generation dendrimer G(5) in combination with sticky siRNA therapeutics may constitute a promising gene silencing-based approach for combating castration-resistant prostate tumors or other cancers and diseases, for which no effective treatment currently exists.
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Interfacial tension of oil/water emulsions with mixed non-ionic surfactants: comparison between experiments and molecular simulations

TL;DR: In this paper, the authors investigate oil/water emulsions through interfacial tension using two common non-ionic surfactants, Tween 80 and Span 20, in the concentration range C (0.3 − 1 wt%) well above their respective CMCs.
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Homology Model and Docking-Based Virtual Screening for Ligands of the σ1 Receptor.

TL;DR: This study presents for the first time the 3D model of the σ1 receptor protein as obtained from homology modeling techniques, shows the applicability of this structure to docking-based virtual screening, and defines a computational strategy to optimize the results based on a combination of 3D pharmacophore-based docking and MM/PBSA free energy of binding scoring.